Given the severe impact of low bone density (LBD) on public health, and to avoid the potential damage of X-rays-based bone density measurements, this study aimed to develop a scoring system for the mass screening for LBD in women aged 50 years or older, using the basic body check-up items as variables. Five variables, including age, body mass index (BMI), systolic blood pressure (SBP), blood glucose level, and total cholesterol level (TCL), were obtained from medical check-up records of 1525 women aged 50 years or older who had done body examination between 2011 and 2018, and were used to construct a scoring system for the screening for LBD. Multivariate logistic regression was applied to investigate the putative association of the five variables with LBD. A scoring system was derived from the regression model to discriminate persons at risk of LBD from low-risk persons. An artificial neural network (ANN) model was also applied to the same task. Precision, recall, Fl-score, and c-statistic were adopted as evaluation metrics. Age, BMI, SBP, glucose, and TCL were significantly associated with the risk of LBD. Precision, recall, c-statistic, and F1-score of the proposed scoring system were 0.66, 0.83, 0.73, and 0.74, respectively. ANNs achieved better performances in terms of all measurements. This study demonstrates the feasibility of using routine body check-up items to estimate LBD risk. Different from X-rays-based instruments, the scoring system derived from this study may serve as a postcheck-up mass screening tool to enable health practitioners to identify individuals at a risk of LBD efficiently and nonintrusively.